knitr::include_graphics('img/landsat.png')
landsat_file <- here('data/Landsat7.tif')
ls_1 <- raster(landsat_file)
ls_1
## class : RasterLayer
## band : 1 (of 5 bands)
## dimensions : 1758, 3701, 6506358 (nrow, ncol, ncell)
## resolution : 30, 30 (x, y)
## extent : -59564.57, 51465.43, -404675.9, -351935.9 (xmin, xmax, ymin, ymax)
## crs : +proj=aea +lat_0=0 +lon_0=-120 +lat_1=34 +lat_2=40.5 +x_0=0 +y_0=-4000000 +datum=NAD83 +units=m +no_defs
## source : /Users/logankozal/github/ESM244/labs/esm244_w2021_lab6_rasters/data/Landsat7.tif
## names : Landsat7
## values : 0, 255 (min, max)
plot(ls_1)
ls_2 <- raster(landsat_file, band = 2)
ls_3 <- raster(landsat_file, band = 3)
ls_4 <- raster(landsat_file, band = 4)
ls_stack <- raster::stack(landsat_file)
ls_stack
## class : RasterStack
## dimensions : 1758, 3701, 6506358, 5 (nrow, ncol, ncell, nlayers)
## resolution : 30, 30 (x, y)
## extent : -59564.57, 51465.43, -404675.9, -351935.9 (xmin, xmax, ymin, ymax)
## crs : +proj=aea +lat_0=0 +lon_0=-120 +lat_1=34 +lat_2=40.5 +x_0=0 +y_0=-4000000 +datum=NAD83 +units=m +no_defs
## names : Landsat7.1, Landsat7.2, Landsat7.3, Landsat7.4, Landsat7.5
## min values : 0, 0, 0, 0, 0
## max values : 255, 255, 255, 255, 255
# grouping cells to make it less memory intensive
ls_1 <- raster::aggregate(ls_1, fact = 3, fun = mean)
ls_2 <- raster::aggregate(ls_2, fact = 3, fun = mean)
ls_3 <- raster::aggregate(ls_3, fact = 3, fun = mean)
ls_4 <- raster::aggregate(ls_4, fact = 3, fun = mean)
ls_4
## class : RasterLayer
## dimensions : 586, 1234, 723124 (nrow, ncol, ncell)
## resolution : 90, 90 (x, y)
## extent : -59564.57, 51495.43, -404675.9, -351935.9 (xmin, xmax, ymin, ymax)
## crs : +proj=aea +lat_0=0 +lon_0=-120 +lat_1=34 +lat_2=40.5 +x_0=0 +y_0=-4000000 +datum=NAD83 +units=m +no_defs
## source : memory
## names : Landsat7
## values : 4.888889, 255 (min, max)
plot(ls_1, col=hcl.colors(n = 100, palette = 'Blues 2'))
plot(ls_2, col=hcl.colors(n = 100, palette = 'Greens 2'))
plot(ls_3, col=hcl.colors(n = 100, palette = 'Reds 2'))
plot(ls_4, col=hcl.colors(n = 100, palette = 'Reds 2'))
#Casey prepared this in advance
sbc_sf <- read_sf(here('data/county.shp')) %>%
st_transform(crs(ls_1))
sbc_rast <- fasterize::fasterize(sbc_sf, ls_1, field = 'OBJECTI')
plot(sbc_rast)
writeRaster(sbc_rast, 'data/county.tif')
#mask everything not land using mask casey preprepared
sbc_rast <- raster(here('data/county.tif'))
plot(ls_3)
raster::mask(ls_3, sbc_rast) %>% plot()
ls_3 <- mask(ls_3, sbc_rast)
ls_4 <- mask(ls_4, sbc_rast)
vec1 <- 1:5
vec1
## [1] 1 2 3 4 5
vec1 * 2
## [1] 2 4 6 8 10
vec1 ^2
## [1] 1 4 9 16 25
ls_3
## class : RasterLayer
## dimensions : 586, 1234, 723124 (nrow, ncol, ncell)
## resolution : 90, 90 (x, y)
## extent : -59564.57, 51495.43, -404675.9, -351935.9 (xmin, xmax, ymin, ymax)
## crs : +proj=aea +lat_0=0 +lon_0=-120 +lat_1=34 +lat_2=40.5 +x_0=0 +y_0=-4000000 +datum=NAD83 +units=m +no_defs
## source : memory
## names : Landsat7
## values : 8.444444, 255 (min, max)
ls_3 * 2
## class : RasterLayer
## dimensions : 586, 1234, 723124 (nrow, ncol, ncell)
## resolution : 90, 90 (x, y)
## extent : -59564.57, 51495.43, -404675.9, -351935.9 (xmin, xmax, ymin, ymax)
## crs : +proj=aea +lat_0=0 +lon_0=-120 +lat_1=34 +lat_2=40.5 +x_0=0 +y_0=-4000000 +datum=NAD83 +units=m +no_defs
## source : memory
## names : Landsat7
## values : 16.88889, 510 (min, max)
log(ls_3)
## class : RasterLayer
## dimensions : 586, 1234, 723124 (nrow, ncol, ncell)
## resolution : 90, 90 (x, y)
## extent : -59564.57, 51495.43, -404675.9, -351935.9 (xmin, xmax, ymin, ymax)
## crs : +proj=aea +lat_0=0 +lon_0=-120 +lat_1=34 +lat_2=40.5 +x_0=0 +y_0=-4000000 +datum=NAD83 +units=m +no_defs
## source : memory
## names : layer
## values : 2.133509, 5.541264 (min, max)
log(ls_3); plot(log(ls_3))
## class : RasterLayer
## dimensions : 586, 1234, 723124 (nrow, ncol, ncell)
## resolution : 90, 90 (x, y)
## extent : -59564.57, 51495.43, -404675.9, -351935.9 (xmin, xmax, ymin, ymax)
## crs : +proj=aea +lat_0=0 +lon_0=-120 +lat_1=34 +lat_2=40.5 +x_0=0 +y_0=-4000000 +datum=NAD83 +units=m +no_defs
## source : memory
## names : layer
## values : 2.133509, 5.541264 (min, max)
vec2 <- 6:10
vec1+vec2
## [1] 7 9 11 13 15
ls_3+ls_4
## class : RasterLayer
## dimensions : 586, 1234, 723124 (nrow, ncol, ncell)
## resolution : 90, 90 (x, y)
## extent : -59564.57, 51495.43, -404675.9, -351935.9 (xmin, xmax, ymin, ymax)
## crs : +proj=aea +lat_0=0 +lon_0=-120 +lat_1=34 +lat_2=40.5 +x_0=0 +y_0=-4000000 +datum=NAD83 +units=m +no_defs
## source : memory
## names : layer
## values : 41, 498.7778 (min, max)
raster:calc()ls_stack <- stack(ls_1, ls_2, ls_3, ls_4)
ls_mean <- raster::calc(ls_stack,fun = mean, na.rm = FALSE)
plot(ls_mean)
## Analysis # NVDI
knitr::include_graphics('img/spectrum.png')
knitr::include_graphics('img/ir_photo.jpg')
\[NDVI = \frac{NIR -RED}{NIR + RED}\]
ndvi <- (ls_4 - ls_3)/(ls_4 +ls_3)
plot(ndvi, col=hcl.colors(100, 'Grays'))
is_forest <- function(x, thresh = .3) {
y <- ifelse(x >= thresh, 1, NA)
return(y)
}
forest <- calc(ndvi, fun = is_forest)
plot(forest, col = 'green')
ggplot and rastersndvi_df <- raster::rasterToPoints(ndvi) %>%
as.data.frame()
forest_df <- raster::rasterToPoints(forest) %>%
as.data.frame()
ggplot(data = ndvi_df, aes(x=x, y=y, fill=layer))+
geom_raster()+
geom_raster(data = forest_df, fill = 'green') +
coord_sf(expand = 0)+
scale_fill_gradient(low="black", high = 'white')+
theme_void()+
theme(panel.background = element_rect(fill = 'slateblue4'))
## Warning: Raster pixels are placed at uneven vertical intervals and will be
## shifted. Consider using geom_tile() instead.